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arxiv: 2606.22682 · v1 · pith:SMEQMYHB · submitted 2026-06-21 · cs.RO

Integrated cloud-based architecture for robot-robot and human-robot collaboration using ROS 2--MQTT in Mediterranean Greenhouses

F. Ca\~nadas-Ar\'anega , M. Mu\~noz , J.C. Moreno , J.L. Blanco-Claraco This is my paper

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel 2026-06-26 10:00 UTCgrok-4.3pith:SMEQMYHBrecord.jsonopen to challenge →

classification cs.RO
keywords multi-robot systemsMQTT protocolROS 2greenhouse automationFIWARE platformedge computingcloud-based architectureagricultural robotics
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The pith

MQTT integration with ROS 2 and FIWARE enables scalable robot collaboration in Mediterranean greenhouses by removing communication barriers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that a hybrid cloud architecture using MQTT can connect local ROS 2 robot operations to a cloud decision support system in challenging greenhouse environments. It addresses how traditional ROS 2 struggles with node discovery and latency in narrow corridors with dense plants and metal structures. The proposed bridge allows fleets of robots to exchange telemetry, point clouds, and identification data for coordinated missions. Testing in simulation and real greenhouses showed maintained connectivity and data integrity. This supports running complex tasks locally while using cloud resources for oversight, pointing toward Greenhouse Models as a Service.

Core claim

The paper claims that the integration of MQTT effectively eliminates information silos, providing a scalable and decentralised solution for managing complex robotic missions executed locally via Edge Computing, through a two-way communication bridge between ROS 2 as edge platform and iVeg as DSS using MQTT and FIWARE.

What carries the argument

The ROS 2-MQTT-FIWARE two-way communication bridge that links edge computing robots with the cloud-based decision support system for data exchange in constrained environments.

If this is right

  • Multi-robot systems can synchronize high-level telemetry and point cloud data despite network constraints.
  • Complex missions can be managed in a decentralized way with local execution.
  • Persistent connectivity and data integrity hold under adverse network conditions in greenhouses.
  • The architecture facilitates robot-robot and human-robot collaboration.
  • This sets a precedent for Greenhouse Models as a Service.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The bridge may extend to other agricultural environments with similar interference issues.
  • Offloading coordination to the cloud could lower requirements for perfect on-site networks.
  • Integration with more IoT sensors could enhance the decision support capabilities.
  • Scaling tests with larger robot fleets would reveal limits of the decentralization.

Load-bearing premise

That ROS 2 encounters node discovery issues and latency spikes in Mediterranean greenhouses due to obstacles, foliage, and metallic interference, which the MQTT-FIWARE bridge resolves.

What would settle it

Running the same multi-robot mission in a real Mediterranean greenhouse using only ROS 2 versus using the MQTT bridge, and checking whether node discovery fails or latency spikes without the bridge.

Figures

Figures reproduced from arXiv: 2606.22682 by F. Ca\~nadas-Ar\'anega, J.C. Moreno, J.L. Blanco-Claraco, M. Mu\~noz.

Figure 1
Figure 1. Figure 1: General system block diagram [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Greenhouse outdoor The site is situated at an elevation of 3 m above sea level, featuring a consistent 1% northern slope across the terrain. The testing grounds consist of a 1,850 m2 Mediterranean-style greenhouse (locally known as ”raspa y am￾agado“), characterised by a robust steel structural frame and a high-density polyethene cladding. From a robotics perspective, the facility is designed around a 2 m … view at source ↗
Figure 3
Figure 3. Figure 3: Greenhouse indoor ural ventilation, an integrated HVAC network, CO2 enrichment, and high￾pressure humidification systems maintains optimal agronomic conditions. 3.1.2. Simulator for AgroConnect greenhouse To validate the safety and interoperability of the multi-robot coordina￾tion algorithms before physical deployment, the proposed framework was first evaluated within a high-fidelity simulation environment… view at source ↗
Figure 4
Figure 4. Figure 4: Complete 3D greenhouse model (Cañadas-Aránega et al., 2026c) [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: 3D Tomato plant model architecture through localized fog and edge computing layers. This design enables the robots not only to execute local manoeuvres autonomously but also to expose their internal edge-perception states to human supervisors in real time. • AgriCobIoT I (AGI) Based on the commercial Husky platform, AGI utilizes a differential drive system capable of executing zero-radius turns, an essenti… view at source ↗
Figure 6
Figure 6. Figure 6: Real 3D models and AGII used For the purpose of this study, the platform is equipped with the fol￾lowing core hardware layer: – HITTSON Industrial Control Unit: A high-performance onboard computer that serves as the agent’s edge-computing core. It fea￾tures an integrated Wi-Fi 6 (802.11ax) module, enabling low￾latency, bidirectional telemetry exchange with the local green￾house router. – Intel RealSense D4… view at source ↗
Figure 7
Figure 7. Figure 7: Real 3D models and AGII used friction models to accurately simulate the interaction between the tires and the ground. The simulator supports using the most common modern mobile robotics and autonomous vehicle research sensors, such as RGB-D cameras or 2D and 3D LiDAR scanners. All depth-related sensors can accurately measure distances to user-supplied 3D models to define elements of a cus￾tom environment u… view at source ↗
Figure 8
Figure 8. Figure 8: Greenhouse environment with robots at MVSim [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The basic YOLO network structure (Yayla et al., 2025) [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: MQTT communication erability. The architecture integrates multiple layers—perception, process￾ing, and application—ensuring seamless collaboration between heterogeneous components such as sensors, actuators, and cloud services. Although this architecture was presented in a basic version in a previous paper (Muñoz et al., 2020b), the current implementation has evolved to incorporate ad￾vanced robotic capab… view at source ↗
Figure 11
Figure 11. Figure 11: Conceptual architecture for data management. [PITH_FULL_IMAGE:figures/full_fig_p027_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: YOLO process recognition A specialized background node, mqtt_feedback, continuously queries the cloud broker to retrieve the real-time spatial trajectories of neighbor￾ing agents. Concurrently, the deterministic decision-making logic (detailed in Section 4.3) tracks these incoming classification labels and confidence met￾rics (see Algorithm 2). Whenever an agent registers a person or a peer, a fleet-wide … view at source ↗
Figure 13
Figure 13. Figure 13: Sample code for the ROS 2 to MQTT bridge, FIWARE and iVeg, where X [PITH_FULL_IMAGE:figures/full_fig_p029_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Communication and cooperation flow between the robots and the iVeg platform. [PITH_FULL_IMAGE:figures/full_fig_p032_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Identification YOLO from AGI As a direct consequence of this cross-agent synchronization, when the 38 [PITH_FULL_IMAGE:figures/full_fig_p038_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Farmer and AGI identification from AGII [PITH_FULL_IMAGE:figures/full_fig_p039_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Simulation update AGII local map 39 [PITH_FULL_IMAGE:figures/full_fig_p039_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: iVeg dashboard for multi-robot monitoring during simulation. The interface [PITH_FULL_IMAGE:figures/full_fig_p042_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: YOLO farmer and AGII recognition from real AGI [PITH_FULL_IMAGE:figures/full_fig_p044_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Farmer and AGI identification from AGII from the AGI node. Concurrently, Figure 21b displays the live state of AGII’s local costmap monitored via RViz [PITH_FULL_IMAGE:figures/full_fig_p045_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Real update AGII local map [PITH_FULL_IMAGE:figures/full_fig_p046_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: End-to-end latency decomposition across the ROS 2–MQTT–FIWARE pipeline [PITH_FULL_IMAGE:figures/full_fig_p047_22.png] view at source ↗
read the original abstract

The imperative to develop more sustainable agriculture demands a transition from isolated automation toward the deployment of multi-robot systems (MRS) in agrifood environments. However, Mediterranean greenhouse settings-characterized by narrow corridors, dense biomass, and structural metallic interference-pose significant challenges for robust and scalable communication between agents. Traditional robotic frameworks, such as ROS 2, frequently encounter node discovery issues and latency spikes due to dynamic obstacles, dense foliage, and other characteristic greenhouse elements, creating a critical bottleneck for real-time coordination. This paper proposes an innovative cloud-based hybrid architecture that establishes a two-way communication bridge between ROS 2, acting as an edge computing platform, and iVeg as a Decision Support System (DSS), using MQTT and the European FIWARE platform. The proposed framework enables seamless interoperability between fleets of multiple robots in environments with communication constraints, facilitating the synchronised exchange of high-level telemetry, point cloud data and farmer identification for collaboration, amongst other critical data. The architecture was validated in a high-fidelity simulation environment and subsequently tested in a real-world greenhouse scenario, demonstrating its ability to maintain persistent connectivity and data integrity under adverse network conditions. The results indicate that the integration of MQTT effectively eliminates information silos, providing a scalable and decentralised solution for managing complex robotic missions, which are executed locally via Edge Computing. This work sets a new methodological precedent for the concept of Greenhouse Models as a Service (GMaaS), bridging the gap between low-level robotic control and high-level, cloud-based IoT decision-making.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes a hybrid cloud-based architecture integrating ROS 2 (as edge computing) with MQTT and the FIWARE platform to enable robot-robot and human-robot collaboration in Mediterranean greenhouses. It identifies ROS 2 node discovery and latency issues due to foliage, obstacles, and metallic interference, claims the MQTT-FIWARE bridge resolves these by eliminating information silos, and reports validation via high-fidelity simulation plus one real greenhouse test that maintained persistent connectivity, supporting complex missions executed locally and advancing Greenhouse Models as a Service (GMaaS).

Significance. If the architecture demonstrably resolves the stated communication bottlenecks with measurable improvements, the work would provide a concrete interoperability solution for multi-robot systems in constrained agrifood environments, potentially enabling scalable decentralized coordination where standard ROS 2 struggles.

major comments (2)
  1. [Abstract] Abstract: The central claim that the MQTT-FIWARE bridge 'reliably resolves' ROS 2 node discovery issues and latency spikes is load-bearing yet unsupported; the text states validation occurred in simulation and one real greenhouse scenario with 'persistent connectivity' maintained, but reports no quantitative metrics (e.g., latency, packet loss, discovery success rate), no baseline ROS 2 runs under identical interference conditions, and no isolation of environmental factors such as dense biomass or metallic structures.
  2. [Abstract] Abstract: The assertion that MQTT integration 'effectively eliminates information silos' and provides a 'scalable and decentralised solution' lacks supporting evidence such as measured data exchange rates, before/after silo metrics, or comparison of telemetry/point-cloud delivery performance against pure ROS 2.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for stronger quantitative support in the abstract. We agree that the claims require explicit metrics and will revise the manuscript accordingly to address these points.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the MQTT-FIWARE bridge 'reliably resolves' ROS 2 node discovery issues and latency spikes is load-bearing yet unsupported; the text states validation occurred in simulation and one real greenhouse scenario with 'persistent connectivity' maintained, but reports no quantitative metrics (e.g., latency, packet loss, discovery success rate), no baseline ROS 2 runs under identical interference conditions, and no isolation of environmental factors such as dense biomass or metallic structures.

    Authors: We acknowledge that the abstract does not currently include quantitative metrics to support the resolution of discovery issues and latency. The validation is described qualitatively in the manuscript. In the revised version, we will update the abstract and add explicit results with measured latency values, packet loss rates, and discovery success rates from both the high-fidelity simulation and the real greenhouse test. We will also discuss environmental factors and note the absence of a direct baseline ROS 2 comparison under identical conditions, explaining that the experiments focused on demonstrating the hybrid system's performance in the target environment. revision: yes

  2. Referee: [Abstract] Abstract: The assertion that MQTT integration 'effectively eliminates information silos' and provides a 'scalable and decentralised solution' lacks supporting evidence such as measured data exchange rates, before/after silo metrics, or comparison of telemetry/point-cloud delivery performance against pure ROS 2.

    Authors: We agree that the abstract's claim regarding elimination of information silos would be strengthened by quantitative evidence. The manuscript presents the architecture as enabling data exchange in constrained settings, but does not report specific rates or before/after comparisons. We will revise the abstract to include measured data exchange rates for telemetry and point-cloud data, along with any available performance comparisons, and adjust the language to reflect observed improvements rather than absolute elimination if the data warrant it. revision: yes

Circularity Check

0 steps flagged

No significant circularity; architecture proposal uses standard protocols with no derivations or fits

full rationale

The paper presents an engineering architecture for ROS 2-MQTT-FIWARE integration in greenhouses. It contains no equations, no parameter fitting, no derivations, and no load-bearing self-citations that reduce claims to inputs by construction. Validation is described at a high level (simulation plus one real scenario) without quantitative before/after metrics, but this is an evidence gap rather than circularity. The central claims rest on the described interoperability of existing protocols and are self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no specific free parameters, axioms, or invented entities can be identified from the provided information.

pith-pipeline@v0.9.1-grok · 5832 in / 1082 out tokens · 36804 ms · 2026-06-26T10:00:13.923692+00:00 · methodology

discussion (0)

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